 
Machine learning (ML) has been widely used to help automate decision-making and improve business efficiency. However, traditional ML relies on centralized training approaches, hence leading to data privacy concerns [1]}, [2]}. As mobile application services have achieved remarkable successes, there has been an emerging trend of employing Federated Learning (FL) over wireless mobile network [3]}, [4]}, [5]}. As a large-scale data-intensive distributed system, FL involves a large number of wireless mobile devices to train a shared model given a training time budget and a resource budget.
